OpenRaiser/Intern-Atlas huggingface dataset:
Speeding Up Computers
Venue: arXiv cs.DC 2016
Year: 2016

There are two distinct approaches to speeding up large parallel computers. The older method is the General Purpose Graphics Processing Units (GPGPU). The newer is the Many Integrated Core (MIC) technology . Here we attempt to focus on the MIC technology and point out differences between the two approaches to accelerating supercomputers. This is a user perspective.
Collision Avoidance of Two Autonomous Quadcopters
Venue: arXiv cs.RO 2016
Year: 2016

Traffic collision avoidance systems (TCAS) are used in order to avoid incidences of mid-air collisions between aircraft. We present a game-theoretic approach of a TCAS designed for autonomous unmanned aerial vehicles (UAVs). A variant of the canonical example of game-theoretic learning, fictitious play, is used as a coordination mechanism between the UAVs, that should choose between the alternative altitudes to fly and avoid collision. We present the implementation results of the proposed coordination mechanism in two quad-copters flying in opposite directions.
A software approach to defeating side channels in last-level caches
Venue: arXiv cs.CR 2016
Year: 2016

We present a software approach to mitigate access-driven side-channel attacks that leverage last-level caches (LLCs) shared across cores to leak information between security domains (e.g., tenants in a cloud). Our approach dynamically manages physical memory pages shared between security domains to disable sharing of LLC lines, thus preventing "Flush-Reload" side channels via LLCs. It also manages cacheability of memory pages to thwart cross-tenant "Prime-Probe" attacks in LLCs. We have implemented our approach as a memory management subsystem called CacheBar within the Linux kernel to intervene on such side channels across container boundaries, as containers are a common method for enforcing tenant isolation in Platform-as-a-Service (PaaS) clouds. Through formal verification, principled analysis, and empirical evaluation, we show that CacheBar achieves strong security with small performance overheads for PaaS workloads.
Multi-Hop Cluster based IEEE 802.11p and LTE Hybrid Architecture for VANET Safety Message Dissemination
Venue: arXiv cs.NI 2016
Year: 2016

This paper proposes a hybrid architecture, namely VMaSC-LTE, combining IEEE 802.11p based multi-hop clustering and the fourth generation cellular system, Long Term Evolution (LTE), with the goal of achieving high data packet delivery ratio and low delay while keeping the usage of the cellular architecture at the minimum level. In VMaSC-LTE, vehicles are clustered based on a novel approach named VMaSC: Vehicular Multi-hop algorithm for Stable Clustering. From the clustered topology, elected cluster heads operate as dual-interface nodes with the functionality of IEEE 802.11p and LTE interface to link VANET to LTE network. Using various key metrics of interest including data packet delivery ratio, delay, control overhead and clustering stability, we demonstrate superior performance of the proposed architecture compared to both previously proposed hybrid architectures and alternative routing mechanisms including flooding and cluster based routing via extensive simulations in ns-3 with the vehicle mobility input from the Simulation of Urban Mobility (SUMO). The proposed architecture also allows achieving higher required reliability of the application quantified by the data packet delivery ratio at the cost of higher LTE usage determined by the number of cluster heads in the network.
Symbolic Tensor Calculus -- Functional and Dynamic Approach
Venue: arXiv cs.SC 2016
Year: 2016

In this paper, we briefly discuss the dynamic and functional approach to computer symbolic tensor analysis. The ccgrg package for Wolfram Language/Mathematica is used to illustrate this approach. Some examples of applications are attached.
Graphlet characteristics in directed networks
Venue: arXiv cs.SI 2016
Year: 2016

A number of network structural characteristics have recently been the subject of particularly intense research, including degree distributions, community structure, and various measures of vertex centrality, to mention only a few. Vertices may have attributes associated with them; for example, properties of proteins in protein-protein interaction networks, users' social network profiles, or authors' publication histories in co-authorship networks. In a network, two vertices might be considered similar if they have similar attributes (features, properties), or they can be considered similar based solely on the network structure. Similarity of this type is called structural similarity, to distinguish it from properties similarity, social similarity, textual similarity, functional similarity or other similarity types found in networks. Here we focus on the similarity problem by computing (1) for each vertex a vector of structural features, called signature vector, based on the number of graphlets associated with the vertex, and (2) for the network its graphlet correlation matrix, measuring graphlets dependencies and hence revealing unknown organizational principles of the network. We found that real-world networks generally have very different structural characteristics resulting in different graphlet correlation matrices. In particular, the graphlet correlation matrix of the brain effective network is computed for 40 healthy subjects and common (present in more than 70 percent subjects) dependencies are raveled. Thus, negative correlations are found for 2-node graphlets and 3-node graphlets that are wedges and positive correlations are found only for 3-node graphlets that are triangles. Graphlets characteristics in directed networks could further significantly increase our understanding of real-world networks.
N-ary Error Correcting Coding Scheme
Venue: arXiv cs.LG 2016
Year: 2016

The coding matrix design plays a fundamental role in the prediction performance of the error correcting output codes (ECOC)-based multi-class task. {In many-class classification problems, e.g., fine-grained categorization, it is difficult to distinguish subtle between-class differences under existing coding schemes due to a limited choices of coding values.} In this paper, we investigate whether one can relax existing binary and ternary code design to $N$-ary code design to achieve better classification performance. {In particular, we present a novel $N$-ary coding scheme that decomposes the original multi-class problem into simpler multi-class subproblems, which is similar to applying a divide-and-conquer method.} The two main advantages of such a coding scheme are as follows: (i) the ability to construct more discriminative codes and (ii) the flexibility for the user to select the best $N$ for ECOC-based classification. We show empirically that the optimal $N$ (based on classification performance) lies in $[3, 10]$ with some trade-off in computational cost. Moreover, we provide theoretical insights on the dependency of the generalization error bound of an $N$-ary ECOC on the average base classifier generalization error and the minimum distance between any two codes constructed. Extensive experimental results on benchmark multi-class datasets show that the proposed coding scheme achieves superior prediction performance over the state-of-the-art coding methods.
Enabling Correct Interest Forwarding and Retransmissions in a Content Centric Network
Venue: arXiv cs.NI 2016
Year: 2016

We show that the mechanisms used in the name data networking (NDN) and the original content centric networking (CCN) architectures may not detect Interest loops, even if the network in which they operate is static and no faults occur. Furthermore, we show that no correct Interest forwarding strategy can be defined that allows Interest aggregation and attempts to detect Interest looping by identifying Interests uniquely. We introduce SIFAH (Strategy for Interest Forwarding and Aggregation with Hop-Counts), the first Interest forwarding strategy shown to be correct under any operational conditions of a content centric network. SIFAH operates by having forwarding information bases (FIBs) store the next hops and number of hops to named content, and by having each Interest state the name of the requested content and the hop count from the router forwarding an Interest to the content. We present the results of simulation experiments using the ndnSIM simulator comparing CCN and NDN with SIFAH. The results of these experiments illustrate the negative impact of undetected Interest looping when Interests are aggregated in CCN and NDN, and the performance advantages of using SIFAH.
Negation in SPARQL
Venue: arXiv cs.DB 2016
Year: 2016

This paper presents a thorough study of negation in SPARQL. The types of negation supported in SPARQL are identified and their main features discussed. Then, we study the expressive power of the corresponding negation operators. At this point, we identify a core SPARQL algebra which could be used instead of the W3C SPARQL algebra. Finally, we analyze the negation operators in terms of their compliance with elementary axioms of set theory.
DASA: Domain Adaptation in Stacked Autoencoders using Systematic Dropout
Venue: arXiv cs.CV 2016
Year: 2016

Domain adaptation deals with adapting behaviour of machine learning based systems trained using samples in source domain to their deployment in target domain where the statistics of samples in both domains are dissimilar. The task of directly training or adapting a learner in the target domain is challenged by lack of abundant labeled samples. In this paper we propose a technique for domain adaptation in stacked autoencoder (SAE) based deep neural networks (DNN) performed in two stages: (i) unsupervised weight adaptation using systematic dropouts in mini-batch training, (ii) supervised fine-tuning with limited number of labeled samples in target domain. We experimentally evaluate performance in the problem of retinal vessel segmentation where the SAE-DNN is trained using large number of labeled samples in the source domain (DRIVE dataset) and adapted using less number of labeled samples in target domain (STARE dataset). The performance of SAE-DNN measured using $logloss$ in source domain is $0.19$, without and with adaptation are $0.40$ and $0.18$, and $0.39$ when trained exclusively with limited samples in target domain. The area under ROC curve is observed respectively as $0.90$, $0.86$, $0.92$ and $0.87$. The high efficiency of vessel segmentation with DASA strongly substantiates our claim.
Measuring the Accuracy of Linked Data Indices
Venue: arXiv cs.DB 2016
Year: 2016

Being based on Web technologies, Linked Data is distributed and decentralised in its nature. Hence, for the purpose of finding relevant Linked Data on the Web, search indices play an important role. Also for avoiding network communication overhead and latency, applications rely on indices or caches over Linked Data. These indices and caches are based on local copies of the original data and, thereby, introduce redundancy. Furthermore, as changes at the original Linked Data sources are not automatically propagated to the local copies, there is a risk of having inaccurate indices and caches due to outdated information. In this paper I discuss and compare methods for measuring the accuracy of indices. I will present different measures which have been used in related work and evaluate their advantages and disadvantages from a theoretic point of view as well as from a practical point of view by analysing their behaviour on real world data in an empirical experiment.
Optimal Lagrange Multipliers for Dependent Rate Allocation in Video Coding
Venue: arXiv cs.MM 2016
Year: 2016

In a typical video rate allocation problem, the objective is to optimally distribute a source rate budget among a set of (in)dependently coded data units to minimize the total distortion of all units. Conventional Lagrangian approaches convert the lone rate constraint to a linear rate penalty scaled by a multiplier in the objective, resulting in a simpler unconstrained formulation. However, the search for the "optimal" multiplier, one that results in a distortion-minimizing solution among all Lagrangian solutions that satisfy the original rate constraint, remains an elusive open problem in the general setting. To address this problem, we propose a computation-efficient search strategy to identify this optimal multiplier numerically. Specifically, we first formulate a general rate allocation problem where each data unit can be dependently coded at different quantization parameters (QP) using a previous unit as predictor, or left uncoded at the encoder and subsequently interpolated at the decoder using neighboring coded units. After converting the original rate constrained problem to the unconstrained Lagrangian counterpart, we design an efficient dynamic programming (DP) algorithm that finds the optimal Lagrangian solution for a fixed multiplier. Finally, within the DP framework, we iteratively compute neighboring singular multiplier values, each resulting in multiple simultaneously optimal Lagrangian solutions, to drive the rates of the computed Lagrangian solutions towards the bit budget. We terminate when a singular multiplier value results in two Lagrangian solutions with rates below and above the bit budget. In extensive monoview and multiview video coding experiments, we show that our DP algorithm and selection of optimal multipliers on average outperform comparable rate control solutions used in video compression standards such as HEVC that do not skip frames in Y-PSNR.
Evolving Shepherding Behavior with Genetic Programming Algorithms
Venue: arXiv cs.AI 2016
Year: 2016

We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of animals (sheep) obeying flocking behavior. Our genetic programming algorithm evolves an expression tree that governs the movements of each dog. The operands of the tree are hand-selected features of the simulation environment that may allow the dogs to herd the sheep effectively. The algorithm uses tournament-style selection, crossover reproduction, and a point mutation. We find that the evolved solutions generalize well and outperform a (naive) human-designed algorithm.
The Two-Modular Fourier Transform of Binary Functions
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we provide a solution to the open problem of computing the Fourier transform of a binary function defined over $n$-bit vectors taking $m$-bit vector values. In particular, we introduce the two-modular Fourier transform (TMFT) of a binary function $f:G\rightarrow {\cal R}$, where $G = (\mathbb{F}_2^n,+)$ is the group of $n$ bit vectors with bitwise modulo two addition $+$, and ${\cal R}$ is a finite commutative ring of characteristic $2$. Using the specific group structure of $G$ and a sequence of nested subgroups of $G$, we define the fast TMFT and its inverse. Since the image ${\cal R}$ of the binary functions is a ring, we can define the convolution between two functions $f:G\rightarrow {\cal R}$. We then provide the TMFT properties, including the convolution theorem, which can be used to efficiently compute convolutions. Finally, we derive the complexity of the fast TMFT and the inverse fast TMFT.
Skew-t inference with improved covariance matrix approximation
Venue: arXiv cs.SY 2016
Year: 2016

Filtering and smoothing algorithms for linear discrete-time state-space models with skew-t distributed measurement noise are presented. The proposed algorithms improve upon our earlier proposed filter and smoother using the mean field variational Bayes approximation of the posterior distribution to a skew-t likelihood and normal prior. Our simulations show that the proposed variational Bayes approximation gives a more accurate approximation of the posterior covariance matrix than our earlier proposed method. Furthermore, the novel filter and smoother outperform our earlier proposed methods and conventional low complexity alternatives in accuracy and speed.
On the star-height of factor counting languages and their relationship to Rees zero-matrix semigroups
Venue: arXiv cs.FL 2016
Year: 2016

Given a word $w$ over a finite alphabet, we consider, in three special cases, the generalised star-height of the languages in which $w$ occurs as a contiguous subword (factor) an exact number of times and of the languages in which $w$ occurs as a contiguous subword modulo a fixed number, and prove that in each case it is at most one. We use these combinatorial results to show that any language recognised by a Rees (zero-)matrix semigroup over an abelian group is of generalised star-height at most one.
Towards Seamless Tracking-Free Web: Improved Detection of Trackers via One-class Learning
Venue: arXiv cs.CR 2016
Year: 2016

Numerous tools have been developed to aggressively block the execution of popular JavaScript programs (JS) in Web browsers. Such blocking also affects functionality of webpages and impairs user experience. As a consequence, many privacy preserving tools (PP-Tools) that have been developed to limit online tracking, often executed via JS, may suffer from poor performance and limited uptake. A mechanism that can isolate JS necessary for proper functioning of the website from tracking JS would thus be useful. Through the use of a manually labelled dataset composed of 2,612 JS, we show how current PP-Tools are ineffective in finding the right balance between blocking tracking JS and allowing functional JS. To the best of our knowledge, this is the first study to assess the performance of current web PP-Tools. To improve this balance, we examine the two classes of JS and hypothesize that tracking JS share structural similarities that can be used to differentiate them from functional JS. The rationale of our approach is that web developers often borrow and customize existing pieces of code in order to embed tracking (resp. functional) JS into their webpages. We then propose one-class machine learning classifiers using syntactic and semantic features extracted from JS. When trained only on samples of tracking JS, our classifiers achieve an accuracy of 99%, where the best of the PP-Tools achieved an accuracy of 78%. We further test our classifiers and several popular PP-Tools on a corpus of 4K websites with 135K JS. The output of our best classifier on this data is between 20 to 64% different from the PP-Tools. We manually analyse a sample of the JS for which our classifier is in disagreement with all other PP-Tools, and show that our approach is not only able to enhance user web experience by correctly classifying more functional JS, but also discovers previously unknown tracking services.
Notes on "An Effective ECC based User Access Control Scheme with Attribute based Encryption for WSN"
Venue: arXiv cs.CR 2016
Year: 2016

The rapid growth of networking and communication technologies results in amalgamation of 'Internet of Things' and 'Wireless sensor networks' to form WSNIT. WSNIT facilitates the WSN to connect dynamically to Internet and exchange the data with the external world. The critical data stored in sensor nodes related to patient health, environment can be accessed by attackers via insecure internet. To counterattack this, there is a demand for data integrity and controlled data access by incorporating a highly secure and light weight authentication schemes. In this context, Santanu et al had proposed an attribute based authentication framework for WSN and discussed on its security strengths. In this paper, we do a thorough analysis on Santanu et al scheme, to show that their scheme is susceptible to privileged insider attack and node capture attack. We also demonstrate that Santanu et al scheme consists of major inconsistencies which restrict the protocol execution.
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances
Venue: arXiv cs.CV 2016
Year: 2016

The Extended Functions of Multiple Instances (eFUMI) algorithm is a generalization of Multiple Instance Learning (MIL). In eFUMI, only bag level (i.e. set level) labels are needed to estimate target signatures from mixed data. The training bags in eFUMI are labeled positive if any data point in a bag contains or represents any proportion of the target signature and are labeled as a negative bag if all data points in the bag do not represent any target. From these imprecise labels, eFUMI has been shown to be effective at estimating target signatures in hyperspectral subpixel target detection problems. One motivating scenario for the use of eFUMI is where an analyst circles objects/regions of interest in a hyperspectral scene such that the target signatures of these objects can be estimated and be used to determine whether other instances of the object appear elsewhere in the image collection. The regions highlighted by the analyst serve as the imprecise labels for eFUMI. Often, an analyst may want to iteratively refine their imprecise labels. In this paper, we present an approach for estimating the influence on the estimated target signature if the label for a particular input data point is modified. This "instance influence estimation" guides an analyst to focus on (re-)labeling the data points that provide the largest change in the resulting estimated target signature and, thus, reduce the amount of time an analyst needs to spend refining the labels for a hyperspectral scene. Results are shown on real hyperspectral sub-pixel target detection data sets.
Deep video gesture recognition using illumination invariants
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we present architectures based on deep neural nets for gesture recognition in videos, which are invariant to local scaling. We amalgamate autoencoder and predictor architectures using an adaptive weighting scheme coping with a reduced size labeled dataset, while enriching our models from enormous unlabeled sets. We further improve robustness to lighting conditions by introducing a new adaptive filer based on temporal local scale normalization. We provide superior results over known methods, including recent reported approaches based on neural nets.
An Optimal Polarization Tracking Algorithm for Lithium-Niobate-based Polarization Controllers
Venue: arXiv cs.SY 2016
Year: 2016

We present an optimal algorithm for the three-stage arbitrary polarization tracking using Lithium-Niobate-based Polarization Controllers: device calibration, polarization state rotation, and stabilization. The theoretical model representing the lithium-niobate-based polarization controller is derived and the methodology is successfully applied. Results are numerically simulated in the MATLAB environment.
Convolution in Convolution for Network in Network
Venue: arXiv cs.CV 2016
Year: 2016

Network in Netwrok (NiN) is an effective instance and an important extension of Convolutional Neural Network (CNN) consisting of alternating convolutional layers and pooling layers. Instead of using a linear filter for convolution, NiN utilizes shallow MultiLayer Perceptron (MLP), a nonlinear function, to replace the linear filter. Because of the powerfulness of MLP and $ 1\times 1 $ convolutions in spatial domain, NiN has stronger ability of feature representation and hence results in better recognition rate. However, MLP itself consists of fully connected layers which give rise to a large number of parameters. In this paper, we propose to replace dense shallow MLP with sparse shallow MLP. One or more layers of the sparse shallow MLP are sparely connected in the channel dimension or channel-spatial domain. The proposed method is implemented by applying unshared convolution across the channel dimension and applying shared convolution across the spatial dimension in some computational layers. The proposed method is called CiC. Experimental results on the CIFAR10 dataset, augmented CIFAR10 dataset, and CIFAR100 dataset demonstrate the effectiveness of the proposed CiC method.